This paper tries to put various ways in which Natural Language Processing (NLP) and Software Engineering (SE) can be seen as inter-disciplinary research areas. We survey the current literature, with the aim of assessing use of Software Engineering and Natural Language Processing tools in the researches undertaken. An assessment of how various phases of SDLC can employ NLP techniques is presented. The paper also provides the justification of the use of text for automating or combining both these areas. A short research direction while undertaking multidisciplinary research is also provided.
Diabetic retinopathy (DR) is an eye fixed ill complete by the impairment of polygenic disorder and that we purchased to acknowledge it before of calendar for sensible treatment. On these lines, 2 social occasions were perceived, specifically non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR). During this paper, to dissect diabetic retinopathy, 3 models like Probabilistic Neural framework (PNN), Bayesian Classification and Support vector machine (SVM) square measure pictured and their displays square measure thought-about. The live of the unwellness unfold within the membrane are often recognized by analytic the elements of the membrane. The elements like veins, hemorrhages of NPDR image and exudates of PDR image square measure off from the unrefined photos victimization the icon prepare techniques, fed to the classifier for gathering a complete of 350 structure photos were used, out of that100 were used for designing and 250 pictures were used for testing. Exploratory results show that PNN has an accuracy of 89.6 % Bayes Classifier incorporates a exactness of 94.4% and SVM has an exactitude of 97.6%. What is more our system is equally continue running on 130 pictures open from "DIARETDB0: Evaluation Database and Procedure for Diabetic Retinopathy" and also the results show that PNN incorporates a exactness of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has a precision of 95.38%.
Programming deformity forecast assumes a vital job in keeping up great programming and decreasing the expense of programming improvement. It encourages venture directors to assign time and assets to desert inclined modules through early imperfection identification. Programming imperfection expectation is a paired characterization issue which arranges modules of programming into both of the 2 classifications: Defect– inclined and not-deformity inclined modules. Misclassifying imperfection inclined modules as not-deformity inclined modules prompts a higher misclassification cost than misclassifying not-imperfection inclined modules as deformity inclined ones. The machine learning calculation utilized in this paper is a blend of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive Laplace Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The proposed Algorithm is assessed and indicates better execution and low misclassification cost when contrasted and the 3 algorithms executed independently.
This paper endeavors to develop newer medium of developing research questions by keeping in view both fields of SE and NLP in proper perspectives. An overview of the current state of art research in SE and NLP is presented. This is done by referring to the SE Body of Knowledge (SEBOK). Analogues to SEBOK, there are no separate Body of Knowledge available for the NLP/Computational Linguistics (CL). Hence whatever falls within the category of NLP/CL was considered in framing the research categories from the NLP/CL side. The paper concludes with future scope of the research presented.
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